Linear Models

A Mean Model Approach

Nonfiction, Social & Cultural Studies, Social Science, Statistics, Science & Nature, Mathematics
Cover of the book Linear Models by William R. Moser, Zbynek Sidak, David Aldous, Pranab K. Sen, Elsevier Science
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Author: William R. Moser, Zbynek Sidak, David Aldous, Pranab K. Sen ISBN: 9780080510293
Publisher: Elsevier Science Publication: October 18, 1996
Imprint: Academic Press Language: English
Author: William R. Moser, Zbynek Sidak, David Aldous, Pranab K. Sen
ISBN: 9780080510293
Publisher: Elsevier Science
Publication: October 18, 1996
Imprint: Academic Press
Language: English

Linear models, normally presented in a highly theoretical and mathematical style, are brought down to earth in this comprehensive textbook. Linear Models examines the subject from a mean model perspective, defining simple and easy-to-learn rules for building mean models, regression models, mean vectors, covariance matrices and sums of squares matrices for balanced and unbalanced data sets. The author includes both applied and theoretical discussions of the multivariate normal distribution, quadratic forms, maximum likelihood estimation, less than full rank models, and general mixed models. The mean model is used to bring all of these topics together in a coherent presentation of linear model theory.

Key Features
* Provides a versatile format for investigating linear model theory, using the mean model
* Uses examples that are familiar to the student:
* design of experiments, analysis of variance, regression, and normal distribution theory
* Includes a review of relevant linear algebra concepts
* Contains fully worked examples which follow the theorem/proof presentation

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Linear models, normally presented in a highly theoretical and mathematical style, are brought down to earth in this comprehensive textbook. Linear Models examines the subject from a mean model perspective, defining simple and easy-to-learn rules for building mean models, regression models, mean vectors, covariance matrices and sums of squares matrices for balanced and unbalanced data sets. The author includes both applied and theoretical discussions of the multivariate normal distribution, quadratic forms, maximum likelihood estimation, less than full rank models, and general mixed models. The mean model is used to bring all of these topics together in a coherent presentation of linear model theory.

Key Features
* Provides a versatile format for investigating linear model theory, using the mean model
* Uses examples that are familiar to the student:
* design of experiments, analysis of variance, regression, and normal distribution theory
* Includes a review of relevant linear algebra concepts
* Contains fully worked examples which follow the theorem/proof presentation

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